Download

To run locally, first download the repository of files here:

Then unzip and launch RStudio into that working directory by double-clicking on the shiny-intro.Rproj file.

01_faithful

  • Code: 01_faithful

  • Run from GitHub:

    shiny::runGitHub("bbest/shiny-intro", subdir="01_faithful")

  • Run locally:

    shiny::runApp("01_faithful")

In order to quickly experiment with visualization, we could pull the code from within the rendering function of the Shiny app and set the input list values that would otherwise be set from the user interface…

input = list(bins = 30)

x <- faithful[, 2] 
bins <- seq(min(x), max(x), length.out = input$bins + 1)
    
hist(x, breaks = bins, col = 'darkgray', border = 'white')

02_quakes_mag

library(tidyverse)

input <- list(slider_mag = c(4, 6))

d <- quakes %>%
  filter(
    mag >= input$slider_mag[1],
    mag <= input$slider_mag[2])

hist(d$mag, col = 'darkgray', border = 'white')

  • Code: 02_quakes_mag

  • Run from GitHub:

    shiny::runGitHub("bbest/shiny-intro", subdir="02_quakes_mag")

  • Run locally:

    shiny::runApp("02_quakes_mag")

03_quakes_depth

library(tidyverse)

input <- list(
  select_var = "depth", 
  slider_mag = c(4, 5), 
  slider_depth = c(0, 100))

d <- quakes %>%
  filter(
    mag   >= input$slider_mag[1],
    mag   <= input$slider_mag[2],
    depth >= input$slider_depth[1],
    depth <= input$slider_depth[2])

hist(d[,input$select_var], col = 'darkgray', border = 'white')

04_quakes_map

library(leaflet)
library(glue)

leaflet(data = quakes[1:20,]) %>% addTiles() %>%
  addCircleMarkers(
    radius = ~mag, color = "red", stroke = FALSE, fillOpacity = 0.5,
    popup = ~glue("mag: {mag}<br>depth: {depth} m"), label = ~as.character(mag))

  • Code: 04_quakes_map

  • Run from GitHub:

    shiny::runGitHub("bbest/shiny-intro", subdir="04_quakes_map")

  • Run locally:

    shiny::runApp("04_quakes_map")

05_quakes_dashboard

library(tidyverse)
library(glue)

input <- list(
  select_var   = "depth", 
  slider_mag   = c(4, 5), 
  slider_depth = c(0, 100))

get_df <- function(){
  df <- quakes %>%
    filter(
      mag   >= input$slider_mag[1],
      mag   <= input$slider_mag[2],
      depth >= input$slider_depth[1],
      depth <= input$slider_depth[2])
  df$var <- df[[input$select_var]]
  df
}

df        <- get_df()
bin_width <- min(c(7, length(unique(df$var))))
    
g <- ggplot(df, aes(var)) + 
  geom_histogram(binwidth=bin_width)

plotly::ggplotly(g)

Rmarkdown using Crosstalk

library(crosstalk)
library(leaflet)
library(DT)

# Wrap data frame in SharedData
sd <- SharedData$new(quakes[sample(nrow(quakes), 100),])

# Create a filter input
filter_slider("mag", "Magnitude", sd, column=~mag, step=0.1, width=250)
# Use SharedData like a dataframe with Crosstalk-enabled widgets
bscols(
  leaflet(sd) %>% 
    addTiles() %>% 
    addMarkers(),
  datatable(
    sd, extensions="Scroller", style="bootstrap", class="compact", width="100%",
    options=list(deferRender=TRUE, scrollY=300, scroller=TRUE)))